Here, a predicate is assigned the frame most similar to it, irrespectively of its sentential context.

all instances of the predicate are assigned the same frame, without any attempt at disambiguation.


We formalize the projection task as a graph alignment problem.

we optimize alignments between dependency graphs under an objective function that takes both lexical and structural similarity into account.


If an attribute comes with a subsumption hierarchy, the IA first computes the best value for that attribute, which is defined as the value closest to the basic level value, such that there is no more specific value that rules out more distractors.

the IA favors dog over chihuahua, unless the latter rules out more distractors.


Based on analyses of naturally occurring texts, McCoy and Strube (1999), for example, emphasized the role of topics and discourse structure for pronoun generation, and pointed out that the changes in time scale are a reliable cue for this.

they found that in certain places a definite description was used where a pronoun would have been unambiguous.


Areces, Koller, and Striegnitz (2008) search for referring expressions in a somewhat non-standard way.

their algorithm does not start with one particular target referent: It simply attempts to find the different sets that can be referred to.


the level of information expressing the commitment of relevant sources towards the factual nature of eventualities in text.

it is in charge of conveying whether eventualities are characterized as corresponding to a fact, to a possibility, or to a situation that does not hold in the world.


Event factuality (or factivity) is understood here as the level of information expressing the factual nature of eventualities mentioned in text.

expressing whether they correspond to a fact in the world (Example (1a)), a possibility (Examples 1b,1c), or a situation that does not hold (Example 1d), as is the case with the events denoted by the following underlined expressions: 2 (1) a. Har-Shefi regretted calling the prime minister a traitor.


The notion of hedging is initially defined by Lakoff (1973, page 471) as "words whose job is making things fuzzier or less fuzzy."

he uses this term to analyze linguistic constructions that express degrees of the is a relationship (e.g., is a sort of, in essence/strickly speaking... is...).


Thus, we need to appeal to the notion of nested source as presented in Wiebe, Wilson, and Cardie (2005).

nelles in Example (13) is not a licensed source of the factuality of event e 2 , but nelles according to the author, represented here as nelles author.


In all other cases, the polarity contributed by the marker will interact with that from the context as specified in Table 2.

positive contextual polarity will respect the original polarity denoted by the marker, whereas negative polarity will switch it.


Whereas SIPs express the epistemic attitude of their (logical) subject concerning the degree of certainty of the embedded event, predicates like want or offer denote the role of their subjects as either having some degree of responsibility on the embedded event (e.g., promise/offer to go; force somebody to go), or being in a greater or lesser favorable state towards its accomplishment (e.g., need/want to go).

they express distinctions within the space of deontic modality.


We conceive the factuality profiler as a neutral and naive decoder; neutral in that it takes all sources as equally reliable; and naive, because it assumes that sources are trustworthy, based on the Gricean maxim of quality.

our model assumes that the information presented in the text is true, without questioning anyone's view or adopting a particular side.


19 This is the factuality value available when the parser reaches event e 1 (be aware), which is consequently characterized as PS− according to source s 0 , the text author.

the factuality profile of event e 1 , p be aware e 1 , is the set of factuality values relative to the relevant sources at its level: p be aware e 1 ={ PS−,s 0 } (Step 4).


We use the sentence Mia is not aware that Joe knows Paul is the father to illustrate the set of relevant sources S n identified at each level l n by the previous definition: Definition 1 seems to return an excessive number of sources at level l 2 .

the source chains s j s 0 and s j s m s 0 appear to be redundant, because both of them refer to the same person, Joe.


Clause (ii) defines anchors and cognizers for higher evaluation levels, l n>0 .

anchors are defined as those sources from the previous evaluation level, s ∈ S n−1 , that are not uncommited (Uu) towards the factuality of e n−1 , which is the SIP event embedding l n (in the definition, the notation f (e, s) expresses the factuality assessment made by source s over event e).


An analysis of this pointed to errors in the dependency trees as the possible cause of this behavior.

they seemed to be pairs involving sources mentioned in subordinated clauses that had not been parsed properly and, as a consequence, De Facto could not pair with their corresponding events.


The model is based on the grammatical structuring of factuality in languages such as English, and addresses the three aforementioned challenges.

it rests upon a three-fold distinction of the factuality scale, it acknowledges the possibility of different sources (with potentially contradictory views), and it is strongly grounded on the information provided by linguistic operators (including polarity and modality particles, predicates of different types, and subordination constructions) together with their cross-level interactions.


In addition, any machine learning efforts towards event factuality identification can both train over De Facto's output, as well as benefit from the lexical types and syntactic features it uses when considering options for machine learning algorithm choice and feature engineering decisions.

we believe that the linguistically motivated model we propose here can, in addition to provide actual information on natural language text, help us understand the phenomenon of event factuality and complement data-driven approaches commonly used in the field.


Here, though, we concentrate on the sentence level, and leave the issue of discourse structure for future work.

we capture the reader's judgment about the veridicality of an event after each sentence, independent on whether the judgment will be revised when later information is read.


The question is why this contrast exists.

why do Turkers choose CT+ where FactBank says Uu?


In the case of epistemic uncertainty, it is known that the proposition is neither true nor false: It describes a possible world where the proposition holds but this possible world does not coincide with the speaker's actual world.

it is certain that the proposition is uncertain.


The relation holds and is considered true if the cue modifies the truth value (confidence) of the event; it does not hold and is considered false if the cue does not have any impact on the interpretation of the event.

the pair (hypothesized, inhibits) in Example (8) is an instance of positive relation.


When the target tasks are treated as different (but related), the label distribution p(L|X) is dependent on the domain.

given a domain d, the problem can be formalized as modeling p(L|X) d based on X d , p(X) d and a set of examples: 8 In the context of domain adaptation, there is a target domain t and a source domain s, with labeled data available for both, and the goal is to induce a more accurate target domain model p(L|X) t from {x i,t , l i } ∪ {x i,s , l i } than the one learned from {x i,t , l i } only.


When there is a domain or genre match between source and target (−/+ and +/− rows in Tables 5 and 6), however, and the distance regarding the other is just moderate, the cross-training performance is close to or even better than the target-only results.

the larger amount of source training data balances the differences between the domains.


Comparing the two domain adaptation procedures DA/CUE and DA/ALL, adaptation via transferring only source sentences that contain a target domain cue is, on average, comparable to transferring all the data from the source domain.

when we have a small but sufficient amount of target data available, it is enough to account for source data corresponding to the uncertainty cues we saw in the limited target data set.


Among these training examples, the ratio of positive to negative examples (cues vs. non-cues) is roughly 1:100.

the data is initially very skewed when it comes to class balance.


Turning our attention to the other end of the tail, we find that just over 40 (8%) of the FNs involve tokens for which there is only a single occurrence as a cue in the training data.

these would first appear to be exactly the tokens that we could never get right, given our filtering scheme.


We used a version of the parser that produces flattened trees.

the flattener deletes VP nodes that are immediately dominated by VP or S and noun phrase (NP) nodes that are immediately dominated by PP or NP.


Here, to support progressive restriction of potential carts, each component is easy to name but the entire shape requires a complex description rather than a prenominal modifier-or at least strongly prefers one, as no examples to the contrary were observed in the Fruit Carts corpus described later in this article.

whereas a square with stripes could be either the square with stripes or the striped square, a square with a diamond on the corner is the square with a diamond on the corner but not *the corner-diamonded square.


Instead we use what can be considered a combination of a schema and a bottom-up approach to structure the discourse.

we use the notion of global focus (Grosz and Sidner 1986) and group together propositions according to the kind of information they convey about the graphic.


(main ent(T1-root)!=main ent(T2-root)) Output: a new tree T3 where the root node has two immediate children such that 1. pred(T3-root)←"Same" ∧ main ent(T3-root)← a unique element 2. left child(T3-root)←T1 ∧ right child(T3-root)←T2 In our work, we thus developed a method that would choose a text plan on principled reasoning concerning the resulting text.

we looked to balance sentence complexity and the number of sentences in the generated text.


In the current implementation of the system, the syntactic form of a subsequent mention of an entity is determined based on its salience status in the context.

the backward-looking center of an utterance 29 is replaced with a less informative definite noun phrase after a continue or a retain transition because the backward-looking center remains the same in the latter utterance.


We conducted an evaluation study to measure how adequate and effective the summaries generated by our system are for our purpose of providing the message and high-level knowledge that one would gain from viewing a graphic.

we were interested in (1) what amount of information is retained by a reader from reading the summary generated by our system, (2) whether someone reading the summary garners the most important knowledge conveyed by the graphic, and (3) whether the knowledge gleaned from the summary is consistent with the actual graphic.


Therefore, some kinds of adjectives receive a differentiated treatment.

two main kinds of adjectives are distinguished in WordNet: (1) Descriptive adjectives, akin to our qualitative adjectives, which are organized around antonymy (descriptive adjectives, however, include intensional adjectives).


Our first series of experiments also provides feedback to the question, what is an adequate computational model for regular polysemy?

we have shown that the treatment of regular polysemy in terms of independent classes is not adequate.


We define five feature sets based on different types of linguistic information, to gain further insight into the properties of each class.

we are interested in the properties of event-related adjectives, for which we do not have a description in the linguistic literature.


This observation leads to the main result of this article: TAGs are not closed under strong lexicalization.

there are TAGs that lack a strongly equivalent lexicalized version.


When we train the composite language on the 1.3 billion token corpus, we have to both aggressively prune the parameters of WORD-PREDICTOR and shrink the order of n-gram and m-SLM in order to store them in a supercomputer having 1,000 cores.

for the composite 5-gram/4-SLM model, its size is too big to store, thus we use its approximation, a linear combination of 5-gram/2-SLM and 2-gram/4-SLM.


The development of the large-scale distributed composite language model is in its infancy; we are planning to deepen our research and push this research in its limit.

we plan to integrate more advanced topic language models such as LDA (Blei, Ng, and Jordan 2003) and resort to a hierarchical non-parametric Bayesian model (Teh 2006;Teh and Jordan 2010) for smoothing fractional counts due to latent variables to handle the sparse data problem in Kneser-Ney's sense in a principled manner, thus constructing a family of large-scale distributed composite lexical, syntactic, and semantic language models.


From the construction relating MBOTs and 1-m-LCFRSs follow results about the source and target sides of the translations produced by MBOTs.

our third result is that the translations produced by MBOTs are context-free within the source language, and hence are strictly less powerful than LCFRSs.


We use T Σ (X) to denote the set of arbitrarily sized trees constructed from ranked alphabet Σ having items from set X at some leaf positions.

t Σ (X) is the smallest set such that X ⊂ t Σ (X) and σ(t 1 , .


The class of translations yield(MBOT) is equivalent to yield(1-m-LCFRS), where 1-m-LCFRS is defined to be the class of LCFRS grammars where each rule either is a start rule of the form S → g(S i ), g( e, f ) = e#f , or meets both of the following conditions: r The combination function keeps the two sides of the translation separate.

it must be possible to write where + represents tuple concatenation, for some functions g 1 and g 2 .


In a case like the given text, the heading of a newspaper article such as "Fatal Accident on Highway 17" would get things started.

a word could evoke a frame, and the semantic parser's job would be to find the elements of that frame in the text, sometimes in the same sentence, in positions determined by the grammar of the word, and sometimes in neighboring sentences.


Transparent nouns are nouns that intervene, in a [N1 of N2] structure, between the frame context and the frame-relevant noun.

in examples like wreak this KIND of havoc, drink a DROP of vodka, divorce that JERK of a husband, it is the second (underlined) noun that matters in our understanding of the semantic nature of the Frame Element.


We have two methods for trading recall for precision that will be detailed later in this section, both relying on calculating the cumulative score S i for each of the binary tags at each word position w i .

(using B as the example tag): where sums over all possible tag sequences for sentence w 1 .


We also note that the efficiency gains from all three constraints are not identical.

high precision and quadratic constraints outperforms unary constraints in isolation.


Applying chart constraints to Chinese parsing in Table 8 gives substantially larger accuracy and efficiency gains than English for both the BUBS and Berkeley parser.

the accuracy of the BUBS parser increases by 2.3 points absolute (p = 0.0002), Table 7 English test set results (WSJ Section 23) applying sentence-level high precision and unary constraints to three parsers with parameter settings tuned on development data.


In future work we plan to investigate additional methods to make context-free parsing more efficient.

we believe the dynamic programming chart could be constrained even further if we take into account the population and structure of chart cells at a finer level.


(2011), has shown promise, but we believe that further research in this area would yield addition efficiency gains.

those two papers do not differentiate between non-terminals when opening or closing cells, and we hypothesize that learning separate finite-state classifiers for automatically derived clusters of nonterminals may increase performance.


The traditional view of sentence-level semantics has been that meaning is compositional.

one can derive the meaning of a sentence by carefully composing the meanings of its constituent parts (Heim and Kratzer 1998).


Second, this article presents a wider range of features compared with the features described in our previous study.

we experimented with corpus statistics derived from sub-corpora that were specifically tailored to the predicate instance under consideration.


This improvement persists even when the original LMs are up to ten times larger than the translated ones.

one has to collect ten times more original material in order to reach the same quality as is provided with translated material.


The last hypothesis we test is whether a better fitting language model yields a better machine translation system.

we expect the T-based LMs to outperform the O-based LMs when used as part of machine translation systems.


Table 16 again demonstrates that language models compiled from original texts must be up to ten times larger in order to yield translation quality similar to that of LMs compiled from translated texts.

4 much smaller translated LMs perform better than much larger original ones, and this holds for various LM sizes, both in-domain and out-of-domain.


By event nominalization we mean those nouns that denote an action or process in the same way that a verb does.

as their verbal counterparts, event nominalizations have the aspectual property of dynamicity (Example (7)).


In contrast, those who consider both denotations as senses of the same lexical unit maintain that nominalizations are underspecified lexical units (Pustejovsky 1995), units in which a disjunction of meaning is present (Alonso 2004), or simply lexical units with different senses (Badia 2002).

pustejovsky accounts for the event-result ambiguity in nominalizations by means of an underspecified lexical representation called dotobject, arguing that event-result nominalizations are cases of complementary polysemy: "both senses of a logically polysemous noun seem relevant for the interpretation of the noun in the context, but one sense seems 'focused' for purposes of a particular context" (pustejovsky 1995, page 31).


Within deverbal nouns the authors distinguish between nominalization senses that truly inherit the verb meaning and deverbal noun senses whose denotation is not directly related to the verb meaning.

7 they distinguish between two different types of deverbal nouns but this distinction is not akin to the event-result one.


We also replicated the experiments at sense and lemma level with the subset of 100kw from the already enriched AnCora-ES and the subset of 817 lexical entries from the AnCora-Nom-R3.

we carried out a set of new ML experiments using the simple and binarized features from the nominal and verbal lexicons as well as additional features obtained from the AnCora-ES corpus (the so-called contextual features).


We used ML techniques to build the ADN-Classifier.

we used the J48.Part rule-based classifier, the rule version of the decision-tree classifier C4.5.Rules (Quinlan 1993) as implemented in the Weka toolbox (Witten and Frank 2005).


In that paper they state that this kind of nominalization can denote an event, a state, or an object.

they analyze those -ung nominalizations derived from verbs of saying embedded in PPs introduced by the preposition nach ('to').


As a consequence of Corollary 1, we also observe that our reducing substitutions never replace undefinable daughters of two distinct node-instantiated licensing rules by one and the same constant.

the term-instantiated rules that result from two distinct node-instantiated licensing rules always satisfy the following compatibility relation.


The term component of the augmented categories encodes a reducing substitution ψ for the simulated derivation with the given c-structure.

if a node n in the G F derivation is labeled by X:t:IR, then ψ(n) = t for the corresponding LFG c-structure tree.


Both the depicted substitution ψ and the subtree to which S F expands are related to the original LFG derivation by the construction of the first half of our proof.

ψ is a reducing substitution and the context-free derivation specializes the category label of each node n of the original c-structure.


These strings are constructed by concatenating the Words from the two component edges in the order specified by the grammar rule that justifies the combination.

an edge does not incorporate the justifying rule but instead records a single member of the yield of the subtree beneath the category of the edge.


After years of being ignored by researchers in other areas of computational linguistics and language technology, many of these same researchers are beginning to think that their own work could benefit from treating text as more than just a bag of sentences.

they are beginning to think that discourse offers some low-hanging fruit-achievable improvements in system performance that exploit either aspects of text structure or the context that text establishes and uses for efficient referring and/or predicational expressions.


We demonstrate that this assumption is false and that the accuracy of data-driven dependency parsers can in fact be sensitive to the existence and placement of the dummy root node.

we show that a greedy, left-to-right, arc-eager transition-based parser consistently performs worse when the dummy root node is placed at the beginning of the sentence (following the current convention in data-driven dependency parsing) than when it is placed at the end or omitted completely.


Attempts to replicate the success of parsing English for other languages have often yielded unsatisfactory results.

parsing languages with complex word structure and flexible word order has been shown to require non-trivial adaptation.


Results coming from multilingual parsing evaluation campaigns, such as the CoNLL shared tasks on multilingual dependency parsing, showed significant variation in the results of the same models applied to a range of typologically different languages.

these results demonstrated that the morphologically rich nature of some of those languages makes them inherently harder to parse, regardless of the parsing technique used (Buchholz and Marsi 2006;Nivre et al.


To combat this added ambiguity, they use external information sources.

they show different ways of using information from monolingual and bilingual data sets in a re-ranking framework for improving parsing accuracy.


They present a parsing model based on Probabilistic Linear Context-Free Rewriting Systems (PLCFRS), which implements many of the technological advances that were developed in the context of parsing with PCFGs.

they present a decoding algorithm based on weighted deductive CKY parsing, and use it in conjunction with PLCFRS parameters directly estimated from treebank data.


There are only residues of an inflectional morphology left.

one cannot exclude that architectural or representational modeling decisions established as empirically useful on English data may be favoring the specific language type of English.


Should data-driven syntactic parsing be split into subtasks, and how should they interact?

should morphological analysis (and likewise tokenization, part-of-speech tagging, etc.)


The resulting parser is currently the best constituent parser for German (with or without bilingual features).

we show that the baseline parser without reranking is competitive with the previous state of the art (the Berkeley parser) and that the re-ranking can add an important gain.


We know of no other work that has investigated to what extent monolingual and bilingual features in parse reranking are complementary.

the work on bitext parsing by Burkett and Klein (2008) does not address the question as to whether the effect of monolingual and bilingual features in parse reranking is (partially) additive.


Finally, as more gold standard trees are annotated, the reranking framework should be constantly retrained.

we expect that the effect of the knowledge sources we have presented will be much stronger when starting with less training data.


All our outside estimates are admissible (Klein and Manning 2003a), which means that they never underestimate the actual outside score of an item.

they are too optimistic about the costs of completing the item into an S item spanning the entire input.


The parser can therefore end up in a local maximum that is not the global maximum we are searching for.

those estimates are only figures of merit (FOM).


The different behavior of the LR and the LN estimate raises the question of the trade-off between maintaining optimality and obtaining a higher parsing speed.

it raises the question of whether techniques such as pruning or coarseto-fine parsing (Charniak et al.


Comparing our results with results from the literature is a difficult endeavor, because PLCFRS parsing of NeGra is an entirely new task that has no direct equivalent in previous work.


This is most notably apparent in verbal phrases and sentential levels.

whereas most sentences follow a subject-verb-object order (SVO), OVS and VSO configurations are also possible (counting in the Hebrew Treebank reveals 5,720 SV cases and 2,613 VS cases, compared with 81,135 SV and 3,018 VS constructions in the English WSJ Treebank).


Some of this information interacts with syntax through agreement constraints.

nouns and adjectives should agree in gender and number, and subjects and verbs should agree in gender, number, and person.


This initialization procedure takes the output of the analyzer and assigns a conditional probability distribution P(tag|word) for each word.

it assigns an a priori, context-free likelihood for each analysis of a word (although the word broke can be either a verb in the past tense or an adjective, it is more likely to be the former; such preferences can be modeled as probability distributions, and the initialization procedure attempts to learn the values of these distributions automatically from raw data).


We would like to use the KC Analyzer (Section 2.3.3) to increase the lexical coverage of the treebank-trained parser.

we would like to improve the lexical model P(T → W) of the generative parser.


Instead, it is a separate process that imposes hard constraints on the search space and rules out parts of it completely.

agreement is a part of the parser and not of the grammar.


This exceptional agreement is orthogonal to the form-function inconsistency discussed earlier.

having a sound or broken plural has no bearing on whether the noun is rational or not-and hence whether an adjectival modifier should agree with it by being feminine-singular or -plural.


We use the Columbia Arabic Treebank (CATiB) .

we use the portion converted from Part 3 of the PATB to the CATiB format, which enriches the CATiB dependency trees with full PATB morphological information.


These results suggest that POS tag set accuracy is as important to parsing quality, if not more important, than its relevance.

when designing a parsing model, one might want to consider that in the tradeoff, mediocre accuracy may be worse than mediocre relevance.


In this theory, MWEs are classified according to their global POS tags (noun, verb, adverb, adjective), and described in terms of the sequence of the POS tags of the words that constitute the MWE (e.g., "N de N" garde d'enfant [guard of child] ('daycare'), pied de guerre [foot of war] ('at the ready')) (Gross 1986).

mWEs are represented by a flat structure.


Furthermore, each selected substring included the Chinese bigrams that appear more frequently than those within the remaining sub-string (of its reference CNE).

f7 tends to select only the portion with high frequency bigrams when all related components are aligned.


This is because the current bigram model does not consider the implied restriction on the context surrounding the given CNE.

a given CNE also implies that its left and right adjacent characters should not be a part of CNE (or its left and right adjacent characters must be in a non-NE region).


It also shows that MERT-W significantly outperforms the baseline.

chinese ORG is observed to yield the largest improvement among NE types in both chinese and English, which matches our previous observations that the boundary of a chinese ORG is difficult to identify using only the information from the chinese sentence.


The proposed joint model thus can be used to train the monolingual NE recognition model via semi-supervised learning on a large unlabeled bilingual corpus.

when semi-supervised learning is conducted for learning the NE recognition model, MERT-W is expected to guide the search process for convergence towards the human annotation.


(Lakoff and Johnson 1980) According to CMT, we conceptualize and structure arguments in terms of battle, which systematically influences the way we talk about arguments within our culture.

the conceptual structure behind battle (i.e., that one can shoot, demolish, devise a strategy, win, and so on), is metaphorically transferred onto the domain of argument.


Although there exist both a considerable practical interest and an extensive linguistic literature, dependency syntax has remained something of an island from a formal point of view.

there are relatively few results that bridge between dependency syntax and other traditions, such as phrase structure or categorial syntax.


Although this result is of fundamental importance from a theoretical point of view, its practical usefulness is limited.

hays-Gaifman grammar is restricted to projective dependency structures, which is similar to the familiar restriction to contiguous constituents.


we have: We use standard terminology from graph theory for dependency trees and the relations between their nodes.

for a node u, the set of descendants of u, which we denote by u , is the set of nodes that can be reached from u by following a directed path consisting of zero or more edges.


It is not hard to see that these choices suffice to determine all other endpoints.

each left endpoint l i ,j will be shared either with the left endpoint l h of some component (by constraint c2), or with some right endpoint r i,j (by constraint c4).


The runtime of this test is linear in the number of nodes of D. In a lexicalized LCFRS extracted from a dependency treebank, there is a one-to-one correspondence between the blocks of a node u and the components of the template of the yield function f extracted for u.

the fan-out of f is exactly the block-degree of u.


In order to assess the consequences of different bounds on the fan-out, we now evaluate the block-degree of dependency trees in real-world data.

we look into five dependency treebanks used in the 2006 CoNLL shared task on dependency parsing (Buchholz and Marsi 2006): the Prague Arabic Dependency Treebank (Hajič et al.


While we would claim that compositionality is a generally desirable property, the particular notion of induction is up for discussion.

our interpretation of derivations may not always be in line with how the grammar producing these derivations is actually used.


Although most of the results that we have presented in this article are of a theoretical nature, some of them have found their way into practical systems.

the extraction technique from Section 4 is used by the data-driven dependency parser of Maier and Kallmeyer (2010).


It is in this vein that we develop our present work.

given a set of (x, y) example pairs, where x is an utterance (e.g., a question) and y is the corresponding answer, we wish to learn a mapping from x to y.


Now, the denotation is not just a set, so we need to generalize this operation.

the aggregate operation applied to a denotation forms a set out of the tuples in the first column for each setting of the rest of the columns: The aggregate operation takes the set of arrays A and produces two sets of arrays, A and A , which are unioned (note that the stores do not change).


Certainly, the answer to this question cannot come from A, so it must come from the stores.


The operation is parametrized by a sequence of distinct indices i that specifies the order in which the columns should be processed.

i indexes into the subsequence of columns with non-empty stores.


We now define the augmented trigger set L B+P , which contains more domain-specific information than L B .

for each domain-specific predicate (e.g., city), we manually specify a single prototype word (e.g., city) associated with that predicate.


We now consider an on-line algorithm, stochastic gradient descent (SGD) (Robbins and Monro 1951), which updates the parameters after computing the candidate set for each example.

we iteratively scan through the training examples in a random order.


As Talmy (1988, page 165) puts it, "we take a sentence (or other portion of discourse) to evoke in the listener a particular kind of experiential complex, here to be termed a 'cognitive representation.'"

linguistic terms may not in the first place describe or represent meanings as such, but rather serve as triggers for activating concepts of human experience, which are far richer and more flexible than any lexical entry or formalization could possibly represent.


Here, relations that influence polarity interpretation include coordination, verb-complement, as well as other types of discourse relations.

the presence of a COMPARISON discourse relation, such as contrast or concession (Prasad et al.


Response time to the target is a negative linear function of its similarity in meaning to the prime, and response accuracy is a positive linear function of this similarity.

the more similar in meaning a prime-target pair was judged to be, the faster and more accurately subjects responded.


These models offer an opportunity to model subtle distinctions in meaning between two occurrences of a word in different contexts.

they allow comparisons between two occurrences of a word without having to classify them as having the same sense or different senses.


If something is musical, would you assume it is not dissonant, and vice versa?

would it be unusual for something to be both musical and dissonant?


If something or someone could possibly be either musical or dissonant, is it necessary that it must be either musical or dissonant?

is it true that for things that can be musical or dissonant, there is no third possible state, except perhaps under highly unusual circumstances?


Focusing mostly on Feature-Based Lexicalized Tree-Adjoining Grammars (FB-LTAG) (but using Interaction Grammars [IG] and Multi-Component Tree-Adjoining Grammars [MC-TAG] to illustrate flexibility), we argue that XMG departs from other existing computational frameworks for designing tree-based grammars in three main ways: r First, XMG is a declarative language.

grammaticality is defined in an order-independent fashion by a set of well-formedness constraints rather than by procedures.


In other words, grammaticality is defined in an order-independent fashion by a set of well-formedness constraints rather than by procedures.

xMG permits a fully declarative treatment of diathesis that markedly departs from the procedural rules (called meta-rules or lexical rules) previously used to specify tree-based grammars.


Concretely, node constraints are attribute-value matrices, which contain information to be used when solving tree descriptions to produce grammar trees.

node constraints are used to further restrict the set of models satisfying a tree description.


As we shall see in Section 6, the DYN dimension permits a modular account of the syntax/semantics interface in which linking constraints can be stipulated separately and reused to specify the various diatheses.

the DYN feature structure allows us to extend the scope of some specific variables so that they can be unified with variables (or values) introduced in some other classes of the metagrammar.


The linguistic units (named Content here) defined by the linguist can be abstracted and combined as follows: The first clause states that the linguistic information encoded in Content is abstracted in a class named Name and that this class inherits classes C 1 ,..., C k and exports variables x 1 ,..., x n .

xMG allows for abstraction, inheritance, and variable exports.


We finally show how different solvers can be used to implement various constraints on each of these dimensions (Section 5.3).

we discuss three kinds of extensions implemented in XMG: extension to several grammar formalisms, integration of explicit linguistic generalizations, and inclusion of color-based node marking to facilitate grammar writing.


In our case, the terminal symbols of the underlying DCG are not just facts, but tuples of descriptions.

the DCG clause whose head is canonicalSubject is associated with a tuple of the following form (the dots have to be replaced with adequate descriptions, these can contain unification variables, whose scope is by default local to the clause): In order to allow for an extension of XMG to an arbitrary number of dimensions, instead of compiling XMG classes into a DCG whose accumulator stores tuples with a fixed arity, these classes are compiled into an EDCG (Van Roy 1990).


As illustrated earlier, interpreting XMG's control language in terms of an EDCG yields tuples whose arity is the number of dimensions defined by the linguist, that is, triples of the form SYN, SEM, DYN if syntax, semantics, and the dynamic interface are described.

for each dimension D, XMG includes a constraint solver S D that computes the set of minimal models each dimension is interpreted separately by a specific solver.


For instance, the sub-formula n 1 ≺ + n 2 , which states that node n 1 strictly precedes node n 2 , is translated into: where 15 EqDown x = Eq x Down x for x ∈ {n 1 , n 2 }.

in a valid minimal tree model, the set of nodes below or equal to n 1 is included in the set of nodes (strictly) on the left of n 2 , the set of nodes below or equal to n 2 is included in the set of nodes (strictly) on the right of n 1 , the set of nodes on the right of n 2 is included in the set of nodes on the right of n 1 , and finally the set of nodes on the left of n 1 is included in the set of nodes on the left of n 2 .


Note that in Figure 7, every type of clitic is defined locally (i.e., in a separate class), and that the interactions between these local definitions are handled by XMG using this rank principle, to produce only one valid description (pictured to the right of the arrow).

xMG ordering constraints permit a simple, declarative encoding of the interaction between clitics.


Briefly, in their work, Wintner (2007, 2009) showed that any polarity-based tree description formalism is not associative.

when describing trees in terms of combinations of polarized structures, the order in which the structures are combined matters (i.e., the output structures depend on the combination order).


For instance, if we make the simplifying assumption that the possible argument realizations are limited to those given in Figure 8, the Subject, Object, ByObject, and IndirectObject classes would be defined as follows.

23 we define the Subject class as an abstraction for talking about the set of tree fragments that represent the possible realizations of a subject argument-namely, in Similarly, tree families for non-verbal predicates (adjectives, nouns) can be defined using the abstraction over grammatical functions defined for verbs.


Candito's approach differs, however, from the XMG account in several important ways: r Much of the linguistic knowledge used to determine which classes to combine is hard-coded in the compiler (unlike in XMG, there is no explicit control on class combinations).

there is no clear separation between the linguistic knowledge needed to specify a high-level FB-LTAG description and the algorithm used to compile an actual FB-LTAG from this description.


The description logic used is similar to Candito's.


Our approach for applying selectional preferences to semantic role classification is discriminative.

the SP-based models provide a score for every possible role label given a verb (or preposition), the head word of the argument, and the selectional preferences for the verb (or preposition).


In this case each word is represented by a vector which contains all similar words with weights, where those weights come from first order similarity.

in order to obtain the second-order vector for word w, we need to compute its first order similarity with all other words in the vocabulary.


A simpler approach consists of completely disregarding the verb information while collecting the prepositional preferences.

the selectional preference for a preposition p and role r is defined as the union of all nouns w found as heads of noun phrases embedded in prepositional phrases headed by p and labeled with semantic role r. Then, one can apply any of the variants described in the previous sections to calculate SP(p, r, w).


These simple prep-role preferences largely avoided the sparseness problem while still being able to capture relevant information to distinguish the appropriate roles in many PP arguments.

they proved to be relevant to distinguish between adjuncts of the type "[in New York] Location " vs. "[in Winter] Temporal ."


Nonetheless, we are aware that not taking into account verb information also introduces some limitations.

the simplification could damage the performance on PP core arguments, which are verb-dependent.


For that, SP models will be used in isolation, according to the classification rule in Equation 11, to predict role labels for a set of (predicate, argument-head) pairs.

we are interested in the discriminative power of the semantic information carried by the SPs, factoring out any other feature commonly used by the state-of-theart SRL systems.


The Selectional Preferences implemented in this study are not able to deal with non-nominal argument heads, such us those of NEG, DIS, MOD (i.e., SPs never predict NEG, DIS, or MOD roles); but, in order to replicate the same evaluation conditions of typical PropBank-based SRL experiments all arguments are evaluated.

our SP models don't return any prediction for those, and the evaluation penalizes them accordingly.


Our model is based on a formal notion of textual definition.

we assume a definition contains the following fields (Storrer and Wellinghoff 2006): r The DEFINIENDUM field (DF): this part of the definition includes the definiendum (that is, the word being defined) and its modifiers (e.g., "In computer science, a pixel"); r The DEFINITOR field (VF): which includes the verb phrase used to introduce the definition (e.g., "is"); r The DEFINIENS field (GF): which includes the genus phrase (usually including the hypernym, e.g., "a dot"); r The REST field (RF): which includes additional clauses that further specify the differentia of the definiendum with respect to its genus (e.g., "that is part of a computer image").


Let we determine the alignment between sentence s j and each sentence s k ∈ C i such that k < j according to the following dynamic programming formulation (Cormen, Leiserson, and Rivest 1990, pages 314-319): where The matching score S a,b is calculated on the generalized sentences s k and s j as follows: where t k,a and t j,b are the a-th and b-th tokens of s k and s j , respectively.

the matching score equals 1 if the a-th and the b-th tokens of the two generalized sentences have the same word class.


The aim of this constraint is to maintain a balance between the height of a concept in the tree-like taxonomy and that of the hypernym considered for addition.

we want to avoid the connection of an overly abstract concept with an overly specific one.


Adding second-order co-occurrences (lines 6-11): Optionally, we create an auxiliary copy V (0) of V. For each word w ∈ V (0) we augment V with those words w which are strongly related to w in the text corpus.

we add w to V if both Equations (2) are satisfied for the pair of words w and w .


In fact, a co-occurrence graph G q = (V, E) for a query q contains: (i) vertices w ∈ V corresponding to words highly related to q, and (ii) edges e ∈ E representing co-occurrence relations between vertices (i.e., words) in V. The key idea behind graph-based WSI is to obtain a partition contains structurally (i.e., semantically) related vertices (i.e., words).

each vertex set V i is intended to contain only words related to a specific sense of q.


Second, a series of iterations is performed aimed at merging the clusters (lines 5-11).

at each iteration the algorithm analyzes each vertex v in random order and assigns it to the majority class among those associated with its neighbors.


Specifically, at each iteration the algorithm analyzes each vertex v in random order and assigns it to the majority class among those associated with its neighbors.

it assigns each vertex v to the class c that maximizes the sum of the weights of the edges {u, v} incident on v such that c is the class of u, according to the following formula: Algorithm 2 The Chinese Whispers algorithm.


We thus performed a second experiment aimed at quantifying the impact of our Web search result clustering systems on flat-list search engines.

our goal was to determine how many different meanings of a query are covered in the top-ranking results shown to the user.


In any case, this conversation lit a fire that fueled my research for the next 15 or 20 years.

i began looking at texts, trying to understand how we understand them.


of words w. Generally the best explanation for an utterance is that it is an intentional act aimed at conveying information.

we can capture this with the axiom (13) Segment(w, e) ∧ goal(i, c) ∧ cog (c, u, e) ⊃ utter (i, u, w) if w is an interpretable segment of discourse describing a situation e, and a speaker i has the goal c that a hearer u adopt some cognitive stance toward e, then (defeasibly) i will utter to u the string of words w. The first conjunct in the antecedent is the entry point into the informational side of an interpretation: what is the content of the message?


We don't want a "species" sense of range, and a "behavioral" sense and an "epidemiological" sense.

the sort of axiom we need for this is as follows: (20) (∀ x, y, z)range (x, y, z)  x ranges from y to z if and only if there is a scale s with a subscale s 1 whose bottom is y and whose top is z, such that some member u 1 of x is at y, some member u 2 of x is at z, and every member u of x is at some point v in s 1 .


We call these systems divisible transition systems and prove a number of theoretical results about their expressivity and complexity.

we characterize an important subclass called efficient divisible transition systems that parse planar dependency graphs in linear time.


We call such systems divisible transition systems and prove a number of theoretical results about their expressivity (which classes of dependency graphs they can handle) and their complexity (what upper bounds exist on the length of transition sequences).

we show that an important subclass called efficient divisible transition systems derive planar dependency graphs in time that is linear in the length of the sentence using standard inference methods for transition-based dependency parsing.


We then demonstrate how the transition system for planar dependency parsing can be generalized to k-planarity by introducing additional stacks.

we define a two-stack system for 2-planar dependency parsing that is provably correct and has linear complexity.


For this purpose, we use the standard notation by which the restriction of a function f : X → Y to a subset A ⊆ X is written as: Transition systems that can be defined using composition of elementary transitions with restrictions are said to be divisible.

a stack-based transition system S = (C, T, c s , C t ) is divisible if and only if every transition in T is of the form a stack-based transition system is divisible if and only if each of its transitions can be written as a composition of restrictions of the elementary transitions SHIFT, UNSHIFT, REDUCE, LEFT-aRC, and RIGHT-aRC.


Instead, it can generate the same tree in different orders depending on the criterion used to choose a transition at each configuration.

the parser by Goldberg and Elhadad (2010) can be seen as an implementation of this transition system, which uses a training algorithm that assigns a weight to each of the ATTACH-RIGHT(i) EF and ATTACH-LEFT(i) EF transitions in such a way that "easier" (more reliable) attachments are performed first.


In this section, we show how this formulation can also be used to define a novel algorithm.

we can obtain a transition system that will be able to parse any planar dependency graph (regardless of projectivity) if we use all elementary transitions except UNSHIFT directly as the transitions of the system.


In Section 5, we then show how the 1-planar dependency parser described in Section 3.3 can be generalized to handle k-planar dependency graphs by introducing additional stacks.

we present a linear-time transition-based parser that is provably correct for 2-planar dependency trees.


For each vertex's incidence list E(v i ), only a maximum of N prune edges remain and the others are pruned.

the process keeps the N prune /2 edges with the largest positive weight and the N prune /2 with the largest negative weight.


In addition, some coreferences that were correctly solved before this process are now incorrectly classified.

the recall of ungendered pronouns has decreased considerably.


Crucially, our approach is entity-centric-that is, our architecture allows each coreference decision to be globally informed by the previously clustered mentions and their shared attributes.

each deterministic rule is run on the entire discourse, using and extending clusters (i.e., groups of mentions pointing to the same real-world entity, built by models in previous tiers).


Note that some rules change depending on the corpus we use for evaluation.

adjectival forms of nations are valid mentions in the Automated Content Extraction (ACE) corpus (Doddington et al.


In a significant departure from previous work, each model in our framework gets (possibly incomplete) entity information for each mention from the clusters constructed by the earlier coreference models.

each mention m i may already be assigned to an entity E j containing a set of mentions: Unassigned mentions are unique members of their own cluster.


As a baseline, we use a typical mentionpair model where this sharing is disabled.

when two mentions are compared, this model uses only the features that were extracted from the corresponding textual extents.


We compare this model with a single-pass baseline, which uses the same sieves as the multi-pass system but applies all of them at the same time.

for each mention under consideration, we select the first antecedent that matches any of the available sieves.


This multi-pass model goes hand-in-hand with the entity-centric approach.

the higher the quality of mention clusters built in the previous sieves, the better the features extracted from these clusters will be in the current sieve-and, of course, better features drive better clustering decisions in the next sieve, and so on.


Therefore, our system misses most coreference links between event mentions.

for example, in Table 12 the pronoun coreferent with the event mention Support.


First, only a fixed beam of the most probable partial derivations are pursued after each word Shift operation.

after predicting each chunk, 8 we prune the set of partial analyses to some fixed beam width K 1 .


We limit the branching factor at each considered parsing action.

for every partial analysis, we consider only K 2 possible next actions.


These results clearly indicate that the connectivity of latent vectors both within representational layers and across them influences the accuracy of recovering the whole propositional content associated with predicates.

our model connecting the latent vectors within the semantic layer significantly improves both the precision and the recall of the predicted propositions over the model where these connections are removed (second vs. third line).


The success of the proposed model also suggests that the same approach should be applicable to other complex structured prediction tasks.

this extension of the ISBN architecture to weakly synchronized syntactic-semantic derivations is also applicable to other problems where two independent, but related, representations are being learned, such as syntax-based statistical machine translation.


Next, we investigate several ways to improve the translation quality by adapting a translation model to the nature of translationese, thereby making the output of machine translation more similar to actual, human translation.

we create two phrase tables, one for the S → T portion of the corpus, and one for the T → S portion, and combine them into a mixture model using perplexity minimization (Sennrich 2012) to set the model weights.


Most existing adaptation methods focus on selecting in-domain data from a general domain corpus.

perplexity is used to score the sentences in the general-domain corpus according to an in-domain language model.


We rely on features that are motivated by the findings of translation studies, having established their relevance through a comparative analysis of the phrase tables.

we use measures such as translation model entropy, inspired by Koehn, Birch, and Steinberger (2009).


We use each subset to train two phrase-based statistical machine translation (PB-SMT) systems , translating in both directions between the languages in each language pair.

we train two PB-SMTs for each translation task, each based on a parallel corpus produced and translated in a different direction.


Finally, for each target phrase t in the phrase table we compute the ratio of the perplexity of t according to the original language model L o and the perplexity of t with respect to the translated model L t (see Section 4).

the factor F is computed as follows: We apply these techniques to the French-to-English and English-to-French phrase tables built from the concatenated corpora, and use each phrase table to train an SMT system.


Furthermore, improvements in BLEU scores that result from attention to translationese are consistent with human judgments.

a machine translation system that produces translations with higher BLEU scores by taking into account the directionality of translation also produces translations that human judges prefer.


In this section we examine whether these findings have a cumulative effect.

we test if an additional improvement in translation quality can be gained by combining our findings for both the language and the translation model.


The results, which are depicted in Figure 2, are not totally conclusive, but are interesting nonetheless.

in all cases the PPLMIN-2 system exhibits a lower number of singletons than the UNION system; and in all systems except the English-Italian one, the number of singletons produced by the PPLMIN-2 system is lowest.


We achieve this by exploiting the fact that these three concepts are closely related: In order to guarantee that the syntactic structure of a sentence prefix is fully connected, it may be necessary to build phrases whose lexical anchors (the words that they relate to) have not been encountered yet.

the parser needs to predict upcoming syntactic structure in order to ensure connectedness.


The operations of substitution and adjunction are then applied to prediction tree instances in exactly the same way as to canonical trees.

adjoining into a node that carries markers pushes the two markers apart.


We will say for short that an entire node u in τ v matches a node half h if it is clear from the context which half of u matches h. Not all correspondences are useful when performing a verification.

we must require that the region of τ v that matches node halves in τ is contiguous and starts at the root of τ v ; and if some node in τ v has unmatched children, then these must be the rightmost children of their parent.


Given some derived tree of G, we can then build an incremental derivation of G which first builds an unlexicalized version of the derived tree using substitution and adjunction of prediction trees, and then verifies this derived tree from left to right.

anything that TAG can derive, PLTAG can derive incrementally.


The naive algorithm shares this problem with all other incremental parsing algorithms that maintain connected partial structures.

parsing algorithms based on bottom-up dynamic programming are not applicable because they necessarily compute constituents for substrings that are not prefixes.


Nodes originating from different elementary trees have distinct markers to indicate which of them should be verified in a single step.

the nodes at the integration site of two prediction trees has two different markers, as its upper and lower half originate from different elementary trees.


In addition, Prediction Theory incorporates a linking theory that relates model quantities to behavioral data (Demberg and Keller 2009).

there are two components that account for processing difficulty in Prediction Theory.


Some of these predictors are correlated with the measures of interest, leading to collinearity in the models which cannot be removed by centering.

pREDIC-TIONTHEORY is correlated with WORDLENGTH, WORDFREQUENCY, BIGRAMpROB, and pREVWORDFREQ after centering.


r Section 8.5 reports results from varying the merging process.

we compare the novel sequence labeling method with the heuristic methods.


Sag and colleagues designed MRS to meet the competing demands of expressive adequacy, grammatical compatibility, computational tractability, and underspecifiability.

it is a computational semantic formalism that allows grammars like the English Resource Grammar to make explicit exactly as much information about semantic predicate argument structure and quantifier and operator scope as is determined by sentence structure, leaving further ambiguity represented via underspecification rather than enumeration of, for example, the full set of possible quantifier scopings for each item.


Towards the end of his career, together with graduate students at Stanford, Sag turned his attention to developing a clearer understanding of the roles of processing and grammar in determining (un)acceptability.

he argued that the unacceptability associated with phenomena such as "island constraints" derives not from ungrammaticality but in fact from more general processing pressures (Staum and Sag 2008;Hofmeister and Sag 2010;Staum Casasanto, Hofmeister, and Sag 2010;Hofmeister et al.


Section 7.3), this is not always the case.

a fractional solution may be obtained, which is not interpretable as an argument, and therefore it is desirable to have a strategy to recover the exact solution.


This is because Equation (11) has the additional integer constraint on the variables.

any feasible dual point provides an upper bound to the original problem's optimal value.


Our response to the sparsity and polysemy challenges with traditional NLP representations is to seek new representations that allow systems to generalize to previously unseen examples.

we seek representations that permit classifiers to have close to the same accuracy on examples from other domains as they do on the domain of the training data.


Just as important, however, is that the representation must simultaneously make the source and target domains look as similar to one another as possible.

if the labeling function f is the same on the source and target domains, then for every h ∈ H, we can provably bound the error of h on the target domain by its error on the source domain plus a measure of the distance between D S and D T : where the variation divergence d 1 is given by where B is the set of measurable sets under D and D (Ben-David et al.


n-gram representations, which we call n-GRAM-R, model a word type w in terms of the n-gram contexts in which w appears in a corpus.

for word w we generate the vector P(w ww )/P(w), the conditional probability of observing the word sequence w to the left and w to the right of w. Each dimension in this vector represents a combination of the left and right words.


We set the weight w f for feature f proportional to the L 1 norm of CRF parameters related to f in the trained POS tagger.

let θ be the CRF parameters for our trained POS tagger, and let θ f = {θ l,v |l be the state for z i and v be the value for f }.


In this section, we evaluate our learned representations on their ability to capture semantic, rather than syntactic, information.

we investigate a set-expansion task in which we're given a corpus and a few "seed" noun phrases from a semantic category (e.g., Superheroes), and our goal is to identify other examples of the category in the corpus.


Such approaches, however, are included here only if the restrictions on the language are considered an inherent property of the approach and not a shortcoming of its implementation.

the following listing excludes languages whose restrictions are not design decisions of the general approach but practical concessions (e.g., Warren and Pereira 1982).


The data presented in the previous section and in the appendix allow for different kinds of aggregations and analyses.

the classes and properties of the observed languages and the timeline of their evolution are interesting.


In experiments where the dialectal label set is more fine-grained (i.e., LEV, GLF, and EGY instead of simply dialect), we assign to the dialectal sentence the label corresponding to the news source's country of publication.

dialectal sentences in the Jordanian (respectively, Saudi, Egyptian) are given the label LEV (respectively, GLF, EGY).


A more informative accuracy rate disregards the case where only two of the three annotators agreed and the annotator whose accuracy was being evaluated contributed one of those two votes.

an annotator's label would be judged against a majority vote that is independent of that annotator's label.


We note here that most classification errors on dialectal sentences occur when these sentences are mislabeled as being MSA, not when they are misidentified as being in some other incorrect dialect.

dialect→dialect confusion constitutes a smaller proportion of errors than dialect→MSA confusion.


If we choose an incoming hyperedge according to the probability P t (τ(z)) of the resulting tree, holding all other variable assignments fixed, we see that, because P t is uniform, we will choose with uniform probability of 1/m among the m incoming hyperedges at each node.

we will choose among the two incoming hyperedges at the root (node 1) with equal probability, meaning that, over the long run, the sampler will spend half its time in the state for the single tree corresponding to nodes 2 and 3, and only one eighth of its time in each of the four other possible trees.


A simple and convenient choice is to make P z (Z[¬t] | Z[t] = ζ(t)) uniform.

each incoming hyperedge variable with m alternatives assigns each hyperedge probability 1/m.


The following is the full list of the Hiero constraints, taken verbatim from Chiang (2007): r If there are multiple initial phrase pairs containing the same set of alignments, only the smallest is kept.

unaligned words are not allowed at the edges of phrases.


The Duhem-Quine thesis states that theoretical assumptions cannot be tested in isolation, but rather whole systems of theoretical assumptions and auxiliary assumptions are subjected to empirical testing.

if our predictions are not in accordance with our theory, we can only conclude that one of our many theoretical assumptions must be wrong, but we cannot know which one, and we can always modify our system of assumptions, leading to various ways of immunity of theories (Stegmüller 1986).


He recommends that the possibility of using T-non-theoretic strategies to identify observations be made the defining criterion for empirical sciences.

in order to make an empirical statement, the two tiers of a T-theoretical and a T-non-theoretical level are necessary because the use of a single theoretical tier prevents distinguishing empirical statements from those that are not.


This approach has the potential benefit of separating "document" words from "author" words.

words whose use varies across documents are expected to be found in document topics, whereas words whose use varies between authors are expected to be assigned to author topics.


The PAN'11 tuning experiment shows a clearer picture in terms of accuracy differences between different parameter settings than the Judgment experiments.

when we used uninformed uniform priors on the document/author word split (δ (D) = δ (A) = 1), and the same wordin-topic priors for both document and author words ( = 0), the obtained accuracy was comparable to AT-P's accuracy.


However, to the best of our knowledge, DADT-P obtained the best accuracy for a fully supervised method that uses only unigram features.

kourtis and Stamatatos (2011), who obtained the highest accuracy (65.8%), assumed that all the test texts are given to the classifier at the same time and used this additional information with a semi-supervised method, whereas kern et al.


To test our approach in a variety of scenarios, we utilized the GivenX protocol, where each target author has exactly X training samples (Breese, Heckerman, and Kadie 1998).

we performed tenfold cross validation over authors, where we partitioned the authors into ten folds and iterated over the folds, using nine folds as the training folds and the remaining fold as the test fold.


We provided experimental results for authorship attribution methods that are based on three topic models (LDA, AT, and DADT) for several scenarios where the number of authors varies from three to about 20,000.

we showed that in most cases, a probabilistic approach that is based on our DADT model (DADT-P) yields the best results, outperforming methods based on LDA and AT, as well as a Token SVM baseline.


Although infinite latent class models like HDP have the advantage of automatically inferring the number of categorical outcomes K, they are still limited in representing feature-rich objects.

in their original form, they are not able to model the data such that each observable object can be generated from a combination of multiple features.


Another major contribution of this article is an extended description of the unsupervised models for solving event coreference.

we focused on providing further explanations about the implementation of the mIBP framework as well as its integration into the HDP and iHMM models.


This organization of event mentions leads to the idea of creating an event hierarchy as the one illustrated in Figure 1.

this figure depicts the hierarchy of the events described in Example (1).


Each node corresponds to a random variable.

shaded nodes denote observable variables.


The Markov Indian buffet process (Van Gael, Teh, and Ghahramani 2008) defines a distribution over an unbounded set of independent hidden Markov chains, where each chain is associated with a binary latent feature value that evolves over time according to Markov dynamics.

if we denote by M the total number of Markov chains associated with the latent feature values and by T the number of observations, mIBP defines a probability distribution over a binary matrix F with an unbounded number of rows M (M → ∞) and T columns.


Our idea of selecting a finite set of representative feature values for each event mention is based on a heuristic approach that is used after running the mIBP generative process.

by considering the event mention y t , f m one of the feature values that characterizes y t , q m the number of times f m was selected for all mentions during mIBP, and v t a threshold variable for y t such that v t ∼ Uniform(1, max{q m | F m t = 1}), we define the finite set of feature values B t corresponding to the observation y t as: A pictorial representation of this idea is illustrated in Figure 4, where only the feature values f m with the corresponding counts q m above the threshold indicated by v t are selected in B t .


11 We have described a new class of unsupervised, nonparametric Bayesian models designed for the purpose of solving the problem of event coreference resolution.

we have shown how already existing models can be extended in order to relax some of their limitations and how to better represent the event mentions from a particular document collection.


A QG assumes the source sentence and a parse are given and scores possible translations of the source sentence along with their parses.

a quasi-synchronous grammar is a monolingual grammar that derives strings in the target language.


In order to better handle syntactic divergence and obtain the benefits of these two types of models, we use rules that combine phrases and syntax.

our rules use dependencies between phrases rather than words; we call them phrase dependencies.


5 In our model, even though we have additional structure (i.e., the phrase dependency tree τ φ ), we do not want to enforce any additional constraints on the search space.

the space of valid translations is still constrained solely by a standard phrase table.


6 So, even though our phrase dependency rules do not consume parts of the input, we still speak in terms of "rule extraction" because our procedure is similar to rule extraction in other systems and we define feature functions on our rules in a standard way.

we use the extracted rule instances to compute relative frequency estimates for many of the features presented in Section 5.


When actually extracting phrase dependencies, we record additional information from the sentence pairs in which we found them.

for d-consistent phrase dependencies y y y j i , y y y l k (where y y y l k is the parent), we extract tuples of the following form: where I [P] is the indicator function that returns 1 if P evaluates to true and 0 otherwise.


Our simplest probability features use the information in these tuples, but we also extract tuples with more information to support richer features.

we record aligned Table 5 Most frequent Brown cluster phrase dependencies extracted from DE→EN data, shown with their counts.


10 Again, I [P] is the indicator function that returns 1 if P evaluates to true and 0 otherwise.

we include the two target phrases, their aligned source phrases, the direction of the target attachment, the orientation between the source and target phrases (whether the two target phrases are in the same order as their aligned source phrases or swapped), whether a gap is present between the two target phrases, and finally whether a gap is present between the two source phrases.


We note that this rule says nothing about what fills the gap.

the gap-filling material does not have to be translationally equivalent, and indeed in the given sentence pair it is not.


Like the phrase probability features in Moses, many of our feature functions are conditional probabilities computed using relative frequency estimation given the full collection of extracted tuples.

for a tuple α, β , the conditional probability of field α given field β is estimated as where #{ α, β } denotes the count of the tuple α, β in the multiset of extracted tuples.


Instead, we use a feature that considers bounds on the number of target words crossed by each phrase dependency.

the feature sums the maximum number of target words that could be crossed by a particular phrase dependency.


However, for our model we need to score configurations involving phrase dependencies.

for a child-parent phrase dependency φ c , φ d in τ φ , we consider the relationship between π c and π d , the source-side phrases to which φ c and φ d align.


First we find the full set of configurations that are present between any word in one source phrase and any word in the other source phrase.

given a pair of source words, one with index j in source phrase d and the other with index k in source phrase c , we have a parent-child configuration if τ x x x (k) = j; if τ x x x (j) = k, a child-parent configuration is present.


We modified the algorithm to output maximum lengths because we use the maximum lengths to compute the target-side vine grammar features and constraints, as mentioned in Section 5.2.2.

we use a feature f tgt vine that is a target-side analog to fsrc vine but using the Floyd-Warshall maximum path lengths in place of the actual lengths.


Though we are doing lattice reranking rather than k-best reranking, the learning problem for our fine model is similar to that for k-best reranking in that the decoder is exact (i.e., there is no pruning that could lead to different patterns of search error as the parameters change).

phrase lattice generation and pruning (described in Section 6.3.2) only depend on the coarse phrase-based feature weights and the maximum dependency length constraints (described in Section 6.3.3); they do not depend on the fine model parameters.


We have access to supervised parsers for Chinese, German, and English, which we used for our experiments.

we used the Stanford parser (Levy and Manning 2003;Rafferty and Manning 2008) for Chinese and German and TurboParser (Martins et al.


In all previous experiments, we used a source-side limit ω x x x of 15 and a target-side limit ω y y y of 20.

all target-side phrase dependencies may cover a maximum of 20 words in the target sentence, and the number of words between the aligned source phrases can be at most 15.


A passive warden examines all messages exchanged between Alice and Bob but crucially does not modify any message.

we have ignored the possibility of steganographic attacks (Fridrich 2009), via an active warden who deliberately modifies messages in order to thwart any hidden communication.


In addition, for the human evaluation of the security level of our stegosystem, we evaluate the naturalness of generated stego sentences.

we do not investigate the document-level coherence of stego text because this requires sophisticated knowledge of natural language semantics and pragmatics which we consider to be outside the scope of this work.


Similar to synonym substitution, text paraphrasing restates a phrase using different words while preserving the essential meaning of the source material being paraphrased.

text paraphrasing is multi-word substitution.


(2001) generated alternative sentences by adjusting the structural properties of intermediate representations of a cover sentence.

instead of performing lexical substitution directly on the text, the modifications are performed on the syntactic parse tree of a cover sentence.


poor translations have longer codes while better translations have shorter codes.


The reason for also including non-synonymous words during the encoding is because the receiver does not know the cover word and, therefore, we need a method to ensure that the receiver is encoding the same list of words, namely, the same synonym graph, as the sender during the secret recovery.

the sender and the receiver must derive the same synonym graph so that the sender knows the cover word and the receiver knows the stego word.


synonym graphs are then encoded independently.

the encoding of G 1 does not affect the codes assigned to the words in G 2 .


It is worth noting that, although there will be a decrease in recall if more false negatives are obtained from a system, there will not be a negative effect on the value of precision.

from a security perspective, rejecting an acceptable substitute does not damage the quality of stego text.


The results show that for both methods the accuracy, precision, and recall values drop when using fewer n-grams.

among the four combinations, the one including bigram to five-gram frequency counts performs the best across different POS and, therefore, is adopted in the NGM system and the NGM DVG system.


Although a higher precision can be achieved by using a higher threshold value-for example, noun substitutions reach almost 90% precision with threshold equal to 0.9-the large drop in recall means many applicable substitutes are being eliminated.

the trade-off between precision and recall implies the trade-off between imperceptibility and payload capacity for linguistic steganography.


This time, the replacement of shame represents secret bit 0, and the replacement with disgrace represents secret bit 1.

a change must be made in order to embed a secret bit in this case.


1 First, we use existing approaches to MWE extraction to automatically generate training material.

we use our earlier work (Tsvetkov and Wintner 2012) to extract a set of positive and negative MWE candidates from a small parallel corpus, and use them for training a BN that can then extract a new set of MWEs from a potentially much larger monolingual corpus.


Some of these features are commonplace, but others are new, or are implemented in novel ways.

we account for the morphological idiosyncrasy of MWEs using a histogram of the number of inflected forms, in a technique that draws from image processing.


We, too, acknowledge the importance of combining different sources of knowledge in the hard task of MWE identification.

we also believe that collocation measures are highly important for this task, but cannot completely solve the problem: Linguistically motivated features are crucial in order to improve the accuracy of the classifier.


The algorithm hence focuses on misalignments: It trusts the quality of 1:1 alignments (which are further verified with a bilingual dictionary) and searches for MWEs exactly in the areas that word alignment failed to properly align, not relying on the alignment in these cases.

the algorithm views all words that are not included in 1:1 alignments as potential areas in which to search for MWEs, independently of how these words were aligned by the word-aligner.


For each evaluation set (WordNet, DELA English, and DELA French), we divide the number of bigrams in the set that are classified as MWEs by the size of the intersection of the evaluation set with the monolingual corpus.

we exclude from the evaluation those MWEs in the evaluation set that never occur in our corpora.


Further studies conducted in Rosso (2009a, 2009b) have confirmed the importance of considering both language-independent and Arabic-specific features in the NER system.

benajiba, Diab, and Rosso (2009a) studied the impact of SVM, ME, and CRF models using the same approach and features described in benajiba, Diab, and Rosso (2008b).


Precisely because of Liu's different tack on opinion and sentiment analysis, for many readers the book will be a wonderful source of ideas for new problems to work on in the field.

the language of product reviews is quite different from that of other opinion-oriented text (e.g., editorials, blogs, position papers, political arguments, and even movie reviews).


This, and the next chapter, are clearly intended for readers with some familiarity with machine learning who may not be as familiar with the full sweep of models available.

the organization of the material provides good entry points where the reader can find plentiful references if they are curious about the approach.


The summary sections are very good reviews and would suffice to get a general idea of how the task of extracting semantic relations can be approached.

in Section 3.5, the authors provide useful points to consider for navigating the myriad possibilities of models and resources.


Thus G and its string grammars must have the same language L(G) = L(String 1 (G)) = L(String 2 (G)).

by induction on the depth of the derivation trees it is also easy to see that the minimal solution of the f-description of a derivation of a terminal string s is acyclic and single-rooted, and satisfies (root b s ) = (root E) and (root b s ) = (root E'), respectively, if and only if s = s. these grammars both encode their terminal strings in their respective (root b) to (root E)/(root E') paths.


First, we introduce Lipschitz continuity: X can be multi-dimensional space, and |x − y| is the distance between the points x and y.

based on the definition of Lipschitz continuity, we give the definition of the Lipschitz constant ||F|| Lip as follows: the Lipschitz constant ||F|| Lip is the lower bound of the continuity degree that makes the function F Lipschitz continuous.


It is only necessary to prove that the dynamic update is a contraction mapping restricted by this one-following-one dynamic process.

for the proposed ADF update rule, it is only necessary to prove it is a dynamic contraction mapping.


2 Because the current implementation of the CW and AROW methods do not utilize rich edge features, we removed the rich edge features in our systems to make more fair comparisons.

we removed rich edge features in the CRF-ADF setting, and this simplified method is denoted as ADF-noRich.


F SGD is the SGD update rule with the fixed learning rate γ max such that γ max := sup{γ i where γ i ∈ γ γ γ}.

for the SGD update rule F SGD , the fixed learning rate γ max is derived from the ADF update rule.


A different clusstering algorithm is adopted in Lang and Lapata (2011b).

they induce semantic roles via graph partitioning: Each vertex in the graph corresponds to an argument instance and edges represent a heuristically defined measure of their lexical and syntactic similarity.


Besides providing a general modeling framework for semantic role induction, we discuss in detail the linguistic principles guiding our modeling choices and assess their applicability across languages.

we show that the framework presented here (and the aforementioned principles) can be readily applied to English and German with identical parametrizations for both languages and without fundamentally changing the underlying model features, despite major syntactic differences between the two languages.


In our model, we simply use indicator functions which output either 1 or −1 iff feature values are equal and 0 otherwise.

we define four feature similarity functions that we derive from the principles discussed in Section 1.


As discussed earlier, engineering a scoring function that integrates these into a single score without resorting to heuristic judgments on how to weight them poses a major challenge.

it is difficult to weight the contribution of the two forms of positive evidence given by lexical and syntactic similarity.


This separation avoids the difficulty of defining the exact interaction between the two.

we obtain an initial clustering by grouping together all instances which occur in the same syntactic position within a linking-that is, all pairs ( are grouped into the same cluster, assuming that arguments occurring in a specific syntactic position under a specific linking share the same role.


For our role induction problem, this would require a heuristically defined similarity function that combines the similarities on individual features into a single similarity score between instances.

we would collapse the multiple graph layers into a single layer and then partition the resulting single-layer graph according to a standard clustering algorithm.


We do not treat split arguments or co-referential arguments (e.g., in relative clauses).

we ignore arguments with roles preceded by the C-or R-prefix in the gold standard.


Instead, we will look at the induced clusters as a whole and assess their quality in terms of how well they reflect the assumed gold standard.

for each verb, we determine the extent to which argument instances in the clusters share the same gold standard role (purity) and the extent to which a particular gold standard role is assigned to a single cluster (collocation).


Although it is desirable to report performance with a single score such as F1, it is equally important to assess how purity and collocation contribute to this score.

if a hypothetical system were to be used for automatically annotating data, low collocation would result in higher annotation effort and low purity would result in lower data quality.


In order to assess whether differences in performance between two models are statistically significant, we used a sign test.

we obtained a series of score pairs by testing two methods on a subsample of the test data.


To be able to characterize these changes we first examined the consistency of the role assignments created by the two algorithms.

we would expect a verb-argument pair to be mostly assigned to the same cluster (i.e., an argument to bear the same role label for the same verb).


We further grouped the verbs in our data set into different bins according to their polysemy and allowable argument realizations.

we followed Levin's (1993) taxonomy and grouped verbs according to the number of semantic classes they inhabit (e.g., one, two, and so on).


In general, German places fewer constraints on word order (more precisely phrase order) and instead relies on richer morphology to help disambiguate the grammatical functions of linguistic units.

verbal nominal arguments are marked with a grammatical case 6 that directly indicates their grammatical function.


Therefore, we reduced the number of syntactic cues used for cluster initialization in order to avoid creating too many small clusters for which similarities cannot be reliably computed.

only the syntactic position and function word served as cues to initialize our clusters.


The main challenge in evaluating clustering methods is that successful measures should be able to compare solutions found at different levels of granularity.

one cannot assume that there exists one-to-one mapping between the predicted clusters and the gold-standard classes.


The ML estimators of entropy are consistent but heavily negatively biased (see Section 3 for details).

the expectation ofĤ is lower than the true entropy, and this discrepancy increases with the number of clusters m and decreases with the sample size N. When m is comparable to N, the ML estimator is known to be very inaccurate (Paninski 2004).


We define the size of T as |T| = |Q|+|E|.

a PDa T has a bounded stack if there exists K ∈ N such that for any path the number of open parentheses that are not closed along π is bounded.


2011; Roark, Sproat, and Shafran 2011) w 1 , w 2 over the tropical weights w 1 and w 2 with the operations ⊕ and ⊗: The PDA algorithms described in Section 3 are valid under this new semiring because it is commutative and has the path property.

the PDA representing {s} • G is constructed so that the translation grammar score appears in both w 1 and w 2 (i.e., it is duplicated).


This article explores lexicographic semirings and their application to problems in speech and language processing.

we present two instantiations of binary lexicographic semirings, one involving a pair of tropical weights, and the other a tropical weight paired with a novel string semiring we term the categorial semiring.


With a little thought it will be clear that at an appropriate level of abstraction the problem we have just described involves determinization.

the result is deterministic in the sense that for any input, there is a unique path through the lattice.


For this we return to the lexicographic semiring.

we define the T , C lexicographic semiring ( ∪ {∞}, Σ * , ⊕, ⊗,0,1) over a tuple of tropical and left-categorial weights, inheriting their corresponding identity elements.


This can be alleviated by factorizing the stack variable into underspecified stack configurations-that is, model the probability of observing a stack s t as the probability of observing the tail of the stack l t as well as the head of the stack h t given its tail.

the probability of a stack occurrence given the previous stack is factorized as P .


The generated templates are listed on the right side of the figure.

we create a template with the features in each path from the root node to every other internal node in the given decision tree.


An arc that activates one or more negative features is highly unlikely to be in the solution.

if a mention pair has the value yes for one or more negative features, the two mentions are unlikely to be coreferent.


Just as some coreference metrics can be more important than others for certain applications, precision and recall have different values for applications.

for the CoNLL score-which is based on the F β=1 score-the balance between precision and recall is important.


This behavior is because of missing features and sieves for these two languages and also because most of the features and sieves were originally proposed for the English language.

the Arabic candidate pair generation step generates too many recall errors because it is based on only three sieves.


This method for finding monosemous representatives for senses has been in use since 1998, when it was first proposed for the unsupervised acquisition of sense-tagged corpora (Leacock, Chodorow, and Miller 1998).

otrusina and Smrz (2010) exploit WordNet, whose conceptual units are synonym sets, called synsets, which encode the different meanings of words.


Although the uniform distribution can be useful in specific applications such as dictionary disambiguation (Litkowski 2004;Flati and Navigli 2012), or knowledge resource mapping (Navigli and Ponzetto 2012a;Matuschek and Gurevych 2013), in natural text we know that most of the occurrences of an ambiguous word correspond to a usually small subset of predominant senses of that word (Zipf 1949;Sanderson and Van Rijsbergen 1999).

occurrences of an ambiguous word in a real text are usually distributed across its senses according to a highly skewed distribution.


Given that any choice of a different sense distribution for the test data set would have been arbitrary, we selected the uniform one as the approximate average of sense distributions across different domains.

our assumption was that the uniform distribution could be thought of as the fairest different distribution.


To investigate if our WSD systems are biased in favor of more frequent senses, we calculated the recall performance by sense predominance in the Nat test setting (in the Uni test setting we assume no sense predominance).

we separately calculated the recall performance of IMS and UKB on items tagged in the test set with the first (i.e., most frequent) sense of each pseudoword, the second (i.e., second most frequent) sense, and so on.


Naturally distributed data set data set is used for obtaining the additional edges, UKB can hardly cross into 90%.

there is a gap of about 10% resulting from the semi-noisy enrichment of UKB.


In the current set-up, for every instruction the dialogue manager chooses to give the user, the NLG module uses the corresponding template.

there is a one-to-one mapping between the dialogue move and the content of the system utterance; and there is no choice for the NLG module to make in terms of choosing the content of the utterance (except for choosing the referring expressions).


Resources then limited which types of explicit linguistic signals of discourse relations were actually annotated as such.

sentence-initial prepositional phrases with an overt deictic argument (e.g., for that reason, by then) were not included in the set of explicit discourse connectives and hence not systematically annotated because it was felt this could be put off until deictic coreference was annotated more generally.


Point 6involves the possibility of an explicit connective or AltLex expression occurring concurrently with an implicit connective.

a recent unpublished pilot study carried out at the University of Edinburgh (Jiang 2013) used MTurk to show that readers presented with text containing a discourse adverbial also infer the sense associated with a conjunction (coordinating or subordinating), even when no conjunction is present in the text.


One way to limit arguments to only the minimal text needed for a given discourse relation (a minimality principle), was to allow annotators to specify that other text appeared relevant but not necessary to that interpretation.

they could annotate as Sup1, material supplementary to Arg1, as in Example 17, where the preceding question was annotated as relevant to interpreting the question response particle, and as Sup2, material supplementary to Arg2, as in Example 18, where the material enclosed in square brackets was annotated as relevant but not necessary to interpreting the temporal relation expressed with then.


Besides differing in terms of intra-sentential coverage, PropBank and the PDTB also differ in their semantics.

even those PropBank ArgM roles that are closest to discourse relations-ArgM-CAU (causal), ArgM-TMP (temporal), ArgM-PNC (purpose), ArgM-MNR (manner), and ArgM-ADV (adverbial)-differ from the semantics of PDTB senses in several ways.


This paper is concerned with automatic generation of all possible questions from a topic of interest.

we consider that each topic is associated with a body of texts containing useful information about the topic.


With the words in the English sentiment lexicon as seeds, we propose a bilingual word graph label propagation approach to induce sentiment polarities of the unlabeled words in the target language.

we show that both synonym and antonym word relations can be used to build the intra-language relation, and that the word alignment information derived from bilingual parallel sentences can be effectively leveraged to build the inter-language relation.


To solve this problem, we propose a generic approach to addressing the task of cross-lingual sentiment lexicon learning.

we model this task with a bilingual word graph, which is composed of two intra-language subgraphs and an interlanguage subgraph.


Because G incorporates the words in two languages, we call it a Bilingual Word Graph.

the positive weights, W E and W T , represent the synonym intra-language relations, and the negative weights, W E and W T , represent the antonym intra-language relations.


2008) by adding a new regularization term.

besides the positive and negative labels, a dummy label is assigned to each word in the MAD approach.


The resulting algorithm combines the benefits of independent derivations with those of Feature-Based grammars.

we show that it accounts for a range of interactions between dependent vs. independent derivation on the one hand, and syntactic constraints, linear ordering, and scopal vs. nonscopal semantic dependencies on the other hand.


LIGs have been used in the literature (Weir and Joshi 1988;Vijay-Shanker and Weir 1991) to provide a common framework for the extensions of context-free grammars.

vijay-Shanker and Weir (1991Weir ( , 1993 showed a weak equivalence between LIGs, TAGs, and combinatory categorial grammars (Steedman 2000) and proposed a LIG-based polynomial-time CYK recognition algorithm for TAGs and combinatory categorical grammars.


To motivate our approach, we start by considering a simple extension of Schabes and Shieber's LIG framework to FB-TAG, where each LIG rule enforces unifications mimicking those applied in FB-TAG.

let us assume that Type 3 rules  The derived tree (left), the successful standard derivation tree (middle), and the failed dependent derivation tree (right) for all the meerkats.


One key point illustrated by Figure 11 is that whereas multiple adjunction operates on a single node (here η o ), the unification constraints of FB-TAG require that the bottom feature structure of the foot of an auxiliary tree which appears higher in the derived tree (here, β 2 ) unifies with the bottom feature structure of the root of the auxiliary tree appearing immediately below it in the derived tree (here β 1 )-not with that of the root of the node to which it adjoins (here η o ).

while a multiple adjunction on η o operates on η o only, a correct implementation of FB-TAG unification constraints requires keeping track of the feature structures associated with the auxiliary trees successively adjoining to η o .


Before giving the technical details of our parsing algorithm (Section 5), we first highlight some differences between our and Schabes and Shieber's approach.

we show that (i) whereas Schabes and Shieber resort to three distinct mechanisms to account for word order constraints (i.e., selective adjoining constraints, linear precedence statements on derivation trees, and a constraint on parsing), the FB-TAG approach supports a uniform treatment of word order and (ii) our approach straightforwardly accounts for mixed dependent/independent derivations that would require some additional stipulation in Schabes and Shieber's approach.


The resulting algorithm combines the benefits of independent derivations with those of Feature-Based Grammars.

we showed that it accounts for a range of interactions between dependent vs. independent derivation on the one hand, and syntactic constraints, linear ordering, and scopal vs. nonscopal semantic dependencies on the other hand.


As our first step, we show how this syntactic analysis formalism works by presenting an introduction to pregroup algebras.

r The binary operation • is an associative, non-commutative monoid multiplication with the type − • − : P × P → P, such that if a, b ∈ P then a • b ∈ P. P is closed under this operation.


If quick implementation of the formalism described later in this paper is required, it would be useful to be able to leverage the mature state of parsing tools available for other categorial grammars, such as the Clark and Curran (2007) statistical CCG parser, as well as Hockenmaier's CCG lexicon and treebank (Hockenmaier 2003;Hockenmaier and Steedman 2007).

is there any way we can translate at least some subset of CCG types into pregroup types?


These calculi visualize and simplify the axiomatic reasoning within the category to a great extent.

clark, coecke, and Sadrzadeh (2008) and coecke, Sadrzadeh, and clark (2010) show how they depict the pregroup grammatical reductions and visualize the flow of information in composing meanings of single words and forming meanings for sentences.


However, the categorial formalism for which this equivalence has been established differs significantly from the versions of CCG that are in use today.

it allows restriction of combinatory rules on a per grammar basis, whereas modern CCG assumes a universal set of rules, isolating all cross-linguistic variation in the lexicon.